学习ChatGPT原理，人工智能神经网络Python实现

　　import math   import random       # 神经网络3层, 1个隐藏层; 4个input和1个output   network = [4, [16], 1]    population = 50   elitism = 0.2    random_behaviour = 0.1   mutation_rate = 0.5   mutation_range = 2   historic = 0   low_historic = False   score_sort = -1   n_child = 1   # 激活函数   def sigmoid(z):       return 1.0/(1.0+math.exp(-z))   # random number   def random_clamped():       return random.random()*2-1       # ＂神经元＂   class Neuron():       def __init__(self):           self.biase = 0           self.weights = []           def init_weights(self, n):           self.weights = []           for i in range(n):               self.weights.append(random_clamped())       def __repr__(self):           return ＂Neuron weight size:{}  biase value:{}＂.format(len(self.weights), self.biase)       # 层   class Layer():       def __init__(self, index):           self.index = index           self.neurons = []           def init_neurons(self, n_neuron, n_input):           self.neurons = []           for i in range(n_neuron):               neuron = Neuron()               neuron.init_weights(n_input)               self.neurons.append(neuron)           def __repr__(self):           return ＂Layer ID:{}  Layer neuron size:{}＂.format(self.index, len(self.neurons))       # 神经网络   class NeuroNetwork():       def __init__(self):           self.layers = []           # input:输入层神经元数 hiddens:隐藏层 output:输出层神经元数       def init_neuro_network(self, input, hiddens , output):           index = 0           previous_neurons = 0           # input           layer = Layer(index)           layer.init_neurons(input, previous_neurons)           previous_neurons = input           self.layers.append(layer)           index += 1           # hiddens           for i in range(len(hiddens)):               layer = Layer(index)               layer.init_neurons(hiddens[i], previous_neurons)               previous_neurons = hiddens[i]               self.layers.append(layer)               index += 1           # output           layer = Layer(index)           layer.init_neurons(output, previous_neurons)           self.layers.append(layer)           def get_weights(self):           data = { ＂network＂:[], ＂weights＂:[] }           for layer in self.layers:               data[＂network＂].append(len(layer.neurons))               for neuron in layer.neurons:                   for weight in neuron.weights:                       data[＂weights＂].append(weight)           return data           def set_weights(self, data):           previous_neurons = 0           index = 0           index_weights = 0               self.layers = []           for i in data[＂network＂]:               layer = Layer(index)               layer.init_neurons(i, previous_neurons)               for j in range(len(layer.neurons)):                   for k in range(len(layer.neurons[j].weights)):                       layer.neurons[j].weights[k] = data[＂weights＂][index_weights]                       index_weights += 1               previous_neurons = i               index += 1               self.layers.append(layer)           def feed_forward(self, inputs):           for i in range(len(inputs)):               self.layers[0].neurons[i].biase = inputs[i]               prev_layer = self.layers[0]           for i in range(len(self.layers)):               # 第一层没有weights               if i == 0:                   continue               for j in range(len(self.layers[i].neurons)):                   sum = 0                   for k in range(len(prev_layer.neurons)):                       sum += prev_layer.neurons[k].biase * self.layers[i].neurons[j].weights[k]                   self.layers[i].neurons[j].biase = sigmoid(sum)               prev_layer = self.layers[i]               out = []           last_layer = self.layers[-1]           for i in range(len(last_layer.neurons)):               out.append(last_layer.neurons[i].biase)           return out           def print_info(self):           for layer in self.layers:               print(layer)